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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 54-64 © IAEME
54
AN INCREMENTAL TRUST-BASED METHOD FOR ROBUST POSITION
IDENTIFICATION IN WSNs
VINAY T.P1
, SANDHYA P.N2
1
(Assistant Professor, Department of ISE, Channabasaveshwara Institute of Technology,
Tumkur, India)
2
(Assistant Professor, Department of CSE, Channabasaveshwara Institute of Technology,
Tumkur, India)
ABSTRACT
In an Wireless Sensor Networks (WSNs) determining the location of sensors is a basic and
essential knowledge for most WSN algorithms. In this paper, we propose and discuss a technique
that aims to localize all the sensor nodes in the network using 2D trilateration and a security protocol
is used for providing confidentiality and authentication between locators nodes and sensor nodes.
Two issues about unknown nodes secure localization need to be considered. First, the attackers may
disguise as or attack the unknown and anchor nodes to interfere with localization process. Second,
the attackers may forge, replay or modify localization information to make the estimated positions
incorrect.
Keywords: Secure Localization, Verifiable Trilateration, Wireless Sensor Networks.
1. INTRODUCTION
Wireless sensor networks (WSNs) are envisioned to be widely used in medical, military, and
environmental monitoring applications. A future WSN might consist of hundreds to thousands of
deployed sensor nodes which are expected to self-organize into an autonomous network, perform
desired sensing tasks, and react properly to the environment or specific events. Localization is one of
the most important services provided by a WSN, because in most applications we are interested not
only in the types of events that have taken place, but also in where the events have taken place.
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 6, June (2014), pp. 54-64
© IAEME: www.iaeme.com/IJCET.asp
Journal Impact Factor (2014): 8.5328 (Calculated by GISI)
www.jifactor.com
IJCET
© I A E M E

55
2. RELATED WORK
WSN may be deployed in hostile environments where malicious adversaries attempt to spoof
the locations of the sensors by attacking the localization process[1]. For example, an attacker may
alter the distance estimations of a sensor to several reference points, or replay beacons from one part
of the network to some distant part of the network, thus providing false localization information.
Therefore, a secure positioning system must have a mechanism to verify the location claim of
any sensor. Some of the existing secure localization techniques are reviewed below.
2.1 SeRLoc
Lazos and Poovendran propose a novel scheme for localization of nodes in WSNs in
untrusted environments called SeRLoc. SeRLoc is a distributed, range-free, resource-efficient
localization technique in which there is no communication requirement between nodes for location
discovery[2]. SeRLoc is robust against sybil attacks, wormhole attacks and sensor compromise.
2.2 Attack Resistant Location Estimation
Liu, Ning, and Du put forward two range-based robust methods to tolerate malicious attacks
against beacon-based location discovery in sensor networks. The first method, attack-resistant
Minimum Mean Square Estimation, filters out malicious beacon signals. This is accomplished by
examining the inconsistency among location references of different beacon signals, indicated by the
mean square error of estimation, and beating malicious attacks by removing such malicious data. The
second method, voting-based location estimation quantizes the deployment field into a grid of cells
and has each location reference ‘vote’ on the cells in which the node may reside. This method
tolerates malicious beacon signals by adopting an iteratively refined voting scheme. Both methods
survive malicious attacks even if the attacks bypass authentication[5].
2.3 Robust Statistical Methods
Li, Trappe, Zhang, and Nath introduced the idea of being tolerant to attacks rather than trying
to eliminate them by exploiting redundancies at various levels within wireless networks[3].
2.4 SPINE
Capkun and Hubaux devise secure positioning in sensor networks (SPINE), a range-based
positioning system based on verifiable multilateration which enables secure computation and
verification of the positions of mobile devices in the presence of attackers. SPINE works by
bounding the distance of each sensor to at least three reference points[7].
2.5 DRBTS
DRBTS[8] is a distributed reputation and trust-based security protocol aimed at providing a
method for secure localization in sensor networks. In this model, incorrect location information
provided by malicious beacon nodes can be excluded during localization. This is achieved by
enabling beacon nodes to monitor each other and provide information so that sensor nodes can
choose who to trust, based on a majority voting approach. In order to trust beacon node’s
information, a sensor must get votes for its trustworthiness from at least half of their common
neighbors.
2.6 HiRLoc
Lazos and Poovendran propose a high-resolution, range independent localization technique
called HiRLoc. In HiRLoc, sensors passively determine their location without any interaction

56
amongst themselves. HiRLoc also eliminates the need for increased beacon node density and
specialized hardware. Table 1 illustrates the summary of security attacks addressed by each
algorithm.
TABLE 1: Summary of security attacks addressed by each algorithm
Algorithm
Localization Attacks
Distance
Fraud
MafiaFraud
Terrorist
Fraud
Wormhole
Sybil
Spoofing
Jamming
Over
shadowing
Manipulation
Replay
SeRLoc Yes No No Yes Yes Yes No No No No
Attack
Resistant
Location
Estimation
No No No No Yes Yes No No No No
DRBTS Yes No No Yes No Yes No No No No
HiRLoc Yes No No Yes Yes Yes No No No No
3. PROPOSED SYSTEM
An Incremental Trust based Robust Position Identification algorithm contains two phases.
First phase is location estimation in which the sensor node broadcast its ID to locators which comes
in sensor-to locator communication range and those locators perform distance bounding with sensor
node and included within the set LDBs[4]. Then for every locator of set LDBs, trust evaluation value
is estimated by sensor node. If the trust evaluation value is greater than or equal to threshold then it is
included within set LTs. If the number of locators within set LTs is greater than or equal to 3 and any
3 locators of set LTs forms an triangle around sensor, then location of sensor node is estimated
through Verifiable Trilateration. Otherwise localization fails. Second phase is location verification in
which location claim of sensor node is verified by locator through distance bounding protocol.
3.1 An Incremental Trust Based Robust Position Identification
Consider a two tier network which contains randomly deployed sensors to sense the
environment and randomly deployed locators which act as data collection points know their position
via manual configuration or a secure GPS system. The network assumptions are showed in Table 2.
Both sensors and locators perform nanosecond processing and measure time with nanosecond
precision, required for distance bounding[6]. It is assumed that sensor-to sensor communication
range equal to r. Locator-to locator communication range equal to R > r. Sensor-to locator
communication range equal to rsL which is computed as rsL = rG1/γ
, where G denotes the antenna
directivity gain of locators’ antenna and γ denotes the signal attenuation factor. It is assumed that
each sensor s shares a pair wise key K s
Li with each Li to perform cryptographic operations. The
locators which come in the power range are assumed as neighbors in trust evaluation. At least three
locators are required for performing Verifiable Trilateration.

57
An Incremental Trust based Robust Position Identification algorithm contains two phases.
First phase is location estimation in which the sensor node broadcast its ID to locators which comes
in sensor-to locator communication range and those locators perform distance bounding with sensor
node and included within the set LDBs. Then for every locator of set LDBs, trust evaluation value is
estimated by sensor node. If the trust evaluation value is greater than or equal to threshold then it is
included within set LTs. If the number of locators within set LTs is greater than or equal to 3 and any
3 locators of set LTs forms an triangle around sensor, then location of sensor node is estimated
through Verifiable Trilateration[9]. Otherwise localization fails. Second phase is location verification
in which location claim of sensor node is verified by locator through distance bounding protocol.
Table 2 illustrates the network assumption.
TABLE 2: Network Assumptions
Sensors Locators
Area A A
Density ps pL << ps
Antenna Type Omni directional
M directional Antenna with beam
width
M
π2
3.2 Location Identification Phase
Step 1: The sensor s broadcasts its IDs to the locators. s : IDs
Step 2: Any locator Li which can communicate bi-directionally with sensor s performs distance
bounding with s. Distance bounding protocol verifies that sensor s being at a distance d sLi
from Li
cannot claim to be at a distance less than dsLi
LDBs = {Li : || Li – s || ≤ rsL } ...................... (1)
Step 3: For every locator Li which belongs to set LDBs, sensor node s collects the trust evaluation
value of locator Li as in trust model and checks whether the trust evaluation value of locator Li is
greater than or equal to threshold value. If the trust evaluation value of locator Li is greater than or
equal to threshold then the locator is added in the set LTs.
LTs = {Li: Li ∈ LDBs, T sLi
≥ Threshold} ................ (2)
Step 4: Sort the set LTs of locators in the order based on trust evaluation value of locators from high
to low.
Step 5: If | LTs | ≥ 3 then the sensor s performs Verifiable Trilateration with the locators Li ∈ LTs.
Otherwise the localization fails. Sensor s can perform Verifiable Trilateration if it is in the triangle of
three locators.
Step 6: If sensor s estimates its position by Verifiable Trilateration, then it notifies all the locators Li
∈ LDBs, with the transmission of computed position encrypted by the pair wise key and terminates
the algorithm.
Fig.1 shows the Algorithm for An Incremental Trust Based Robust Position Identification
Scheme

58
3.3 Location verification phase
Whenever a sensor node sends data along with the position to the locator, locator needs to
check the claimed position of sensor node. Hence, locator performs distance bounding with s. So, the
sensor cannot claim to be at a distance which is smaller than the actual one.
01: s: broadcast IDs
02: for all Li that receive broadcast from s
03: Li: perform Distance Bounding with s
04: s: define LDBs = { Li : || Li – s || ≤ rsL }
05: endfor
06: for all Li ‫א‬LDBs
07: s: compute Trust Evaluation Value of Li
08: s: define LTs = { Li : Li ‫א‬ LDBs, T sLi
≥ Threshold}
09: s: sort LTs such that
10: LTs = { Li: Li ∈ LTs, T sLi
≥ T sL 1i+
}
11: if (Li ,Lj, Lk) ∈ LTs such that
12: ∃ s inside ∆ Li Lj Lk
13: s: compute ŝ:= Verifiable Trilateration
14: s: notify EKs
Li
(Termination), ∀ Li ∈ LDBs
15: else
16: Localization fails
17: Endfor
Fig. 1: Algorithm for an Incremental Trust Based Robust Position Identification Scheme
4. SECURITY ANALYSIS
4.1 Attacker model
It is assumed that attacker can spoof the location estimated by the sensors. As a result sensors
try to estimate the position than the actual one. However, the attacker does not restrict sensors from
estimating the position. If the localization of sensor node fails, it is believed that it is under attack.
Also it is assumed that attacker is capable of jamming the signals of network entities. However,
jamming signals from all the entities results in failure of localization of the sensor node.
4.2 Wormhole attack
In wormhole attack an attacker receives packet at one point in the network, “tunnels” them to
alternative point in the network. In specified scheme, when the sensor node broadcasts IDs, the
attacker receives this information and tunnels this information to another point in the network and

59
replies from that point. Further, locators sends location information as reply, the attacker collects this
information and tunnels this to another point in the network and replies them.
It is assumed that a set of locators replied to the sensor s is under attack and s performs
Verifiable Trilateration with the three locators Li Lj Lk ∈ LTs such that s lies within ∆ Li Lj Lk. If the
attacker jams the signal from one locator, assume Li and replies as Li after some time, then s still
resides within ∆ Li Lj Lk. Suppose the distance from sensor node s to Li is enlarged then any one of the
other to locators need to reduce the distance. This is impossible due to distance bounding protocol.
Hence, the spoofing of position of s by attacker is not possible. If the attacker jams the signals from
all the locators of set LTs then localization fails.
4.3 Compromised node attack
A network entity is said to be compromised if attacker gains authority of all the information
related to the entity. Suppose if the attacker compromise the locators then it can jam the signals of
those locator which results in failure of localization if significant locators are compromised.
However, if the attacker adds bogus location information to the compromised locators, then this can
be detected by the trust model and those untrustworthy locators are not included in localization.
5. PERFORMANCE ANALYSIS
Case 1: Successful localization
In the case of successful localization there are 3 are more trustworthy locators are there
within the power range of given sensor node. And these trustworthy locators form a triangle around
the given sensor node. So the location of sensor node is computed using the 3 most trustworthy
locators which form triangle around sensor node.
Snapshot 1 shows the initial deployment of sensors and locators. There are 20 nodes in the
snapshot in which 8 nodes are locators, rest are sensor nodes. Sensor nodes are shown in green color
and locators are shown in blue color.
Snapshot 2 shows sensor node 11 is selected for localization. Node 11 is shown in red color
to indicate that this sensor node is selected for localization.
Snapshot 3 shows sensor node 11 broadcasting init message to locators which comes in the
power range of 11.The locators which receive broadcast from sensor node 11 are turned to pink
color.
Snapshot 4 shows the locators which receives broadcast of INIT message from sensor node
11. The locator which comes in sensor-to locator range of sensor node 11 receives this broadcast.
Locators which received broadcast from sensor node 11 are turned as pink, otherwise blue. Here
locators 6, 9, 12, 13, 15, 17 received broadcast from sensor node 11.
Snapshot 5 shows the locators which performed distance bounding with sensor node 11.
Locators which performed distance bounding with sensor node 11 is indicated in orange color, other
locators which are not involved in localization are turned as blue. Here the locators 9, 12, 13, 15, 17
are performing distance bounding with sensor node 11.

60
Snapshot 6 shows locators passed in in trust evaluation by sensor node 11. The locator which
passed in trust evaluation are shown in purple color. Rest of the locators which are not invovled in
localization are shown in blue color.Here the trusted locators are 9, 12, 13, 15, and 17.
Since sensor node 11 is within the triangle formed by trusted locators, it is localized and it is
shown in yellow color in the snapshot 7.
Snapshot 1 Snapshot 2 Snapshot 3
Snapshot 7

61
Locators = {0, 6, 17, 15, 13, 12, 9, 1}
Enter the sensor to be localized: 11
Transmitting INIT Message to locators
Locators in the set LDBs = {17, 15, 13, 12, 9}
Locators in the set LTs = {17, 15, 13, 12, 9}
Verifiable Trilateration computed coordinates for Node 11 is : 375.935, 260.499
Case 2: Unsuccessful localization due to less number of locators in the set LTs.
In the case of unsuccessful localization due to less number of locators in the set LTs, there are
not sufficient trustworthy locators are there to form a triangle around the sensor node. Hence
localization fails.
Snapshot 8 shows sensor node 7 is selected for localization. Node 7 is shown in red color to
indicate that this sensor node is selected for localization.
Snapshot 9 shows sensor node 7 broadcasting init messages to locators which comes in the
power range of 7.The locators which receive broadcast from sensor node 7 are turned to pink color.
Snapshot 10 shows the locators which receives broadcast of INIT message from sensor node
7. The locator which comes in sensor-to locator range of sensor node 7 receives this broadcast.
Locators which received broadcast from sensor node 7 are turned as pink, otherwise blue. Here
locators 0, 1, 6, 9, 13 received broadcast from sensor node 7.
Snapshot 11 shows the locators which performed distance bounding with sensor node 7.
Locators which performed distance bounding with sensor node 7 is indicated in orange color, other
locators which are not involved in localization are turned as blue. Here the locators 0, 1, 6, 9, 13 are
performing distance bounding with sensor node 7.
Snapshot 12 shows locators passed in trust evaluation by sensor node 7. The locator which
passed in trust evaluation are shown in purple color. Rest of the locators which are not invovled in
localization are shown in blue color.Here the trusted locators is 0.
Snapshot 13 shows sensor node 7 which fails to localize. Since there is only one trusted
locator Verifiable Trilateration cannot be performed, localization fails.

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Locators = {0, 6, 17, 15, 13, 12, 9, 1}
Enter the sensor to be localized: 7
Transmitting INIT Message to locators
Locators in the set LDBs = {0, 1, 6, 13, 9}
Locators in the set LTs = {0}
Localization fails.
Case 3: Unsuccessful localization due to failure of Verifiable Trilateration.
In the case of unsuccessful localization due to failure of Verifiable Trilateration, even though
there are sufficient trustworthy locators are there any 3 locators of those trustworthy locators does
not form triangle around the sensor node. Hence localization fails.
6. RESULTS
Simulation parameters are as follows. 20 sensor nodes are randomly placed within the square
area of size 500*500 and locators are randomly placed with varying number. Threshold is set to 0.7.
Fig.2 shows the percentage of trusted locators a sensor can get vs. number of locators for
varying G. Since the trust evaluation depends on G, as the G and number of locators increases, the
percentage of trusted locators also increases.
Fig. 2: Percentage of trusted locators a sensor can get vs. number of locators for varying G
0
10
20
30
40
50
60
70
80
0 5 10 15 20
Percentageoftrustedlocators
aroundasensornode
Number of locators
omni
G=2
G=3
G=4

63
Fig.3 shows the probability, that a sensor can perform Verifiable Trilateration vs. |LTs|. Since
the sensor is included within more than one triangle of locators. The probability of performing
Verifiable Trilateration increases as the number of locators increases.
Fig. 3: Probability, that a sensor can perform Verifiable Trilateration vs. |LTs|
Fig.4 shows percentage of sensors localized vs. number of locators for varying G. The
percentage of sensors localized increases as the number of locators increases. As G increases the
sensor- to locator range also increases. As a result more locators are included in localization process
hence the percentage of sensor getting localized also increases.
Fig. 4: Percentage of sensors localized vs. number of locators for varying G
7. CONCLUSION
Here we proposes a secure localization scheme by modifying Robust Position Estimation
algorithm, which provides more security than the existing one. WSN localization is used to estimate
the locations of the sensors with initially unknown positions in a network using the available priori
knowledge of positions of a few specific sensors in the network. In this work distance bounding
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 2 4 6 8 10 12 14 16 18 20
ProbabilityofperformingVerifiable
Trilateration
|LTs|
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0 5 10 15 20
Percentageofsensorslocalized
Number of locators
Omni
G=2
G=3
G=4
G=5

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protocol is used find the distance enlargement attack, and trust model is employed to rule out the
untrustworthy locators.
And finally Verifiable Trilateration scheme is used to compute the position of the sensor
node in which any three locators which form a triangle around sensor node are selected for
localization process. Verifiable Trilateration and Distance Bounding protocol together resist
wormhole attack and trust model helps to reduce the impact of compromised network entities. In
order to compute the position of the sensor node successfully at least three locators are needed and
any three trusted locators should form triangle around the sensor node.
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